ArticlePloS one2012
A measure of the signal-to-noise ratio of microarray samples and studies using gene correlations.
Article in PloS one, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- LiPLike: towards gene regulatory network predictions of high certainty.Bioinformatics (Oxford, England) · 2020Article
- Identification of core genes and prediction of miRNAs associated with osteoporosis using a bioinformatics approach.Oncology letters · 2019Article
- CRNET: an efficient sampling approach to infer functional regulatory networks by integrating large-scale ChIP-seq and time-course RNA-seq data.Bioinformatics (Oxford, England) · 2018Article
- Identification of genes and pathways in the synovia of women with osteoarthritis by bioinformatics analysis.Molecular medicine reports · 2018Article
- Improving reliability and absolute quantification of human brain microarray data by filtering and scaling probes using RNA-Seq.BMC genomics · 2014Article
- Using expression profiling to understand the effects of chronic cadmium exposure on MCF-7 breast cancer cells.PloS one · 2013Article
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Authors and funding
3 authors.
Funding
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Abstract
backgroundThe quality of gene expression data can vary dramatically from platform to platform, study to study, and sample to sample. As reliable statistical analysis rests on reliable data, determining such quality is of the utmost importance. Quality measures to spot problematic samples exist, but they are platform-specific, and cannot be used to compare studies.
resultsAs a proxy for quality, we propose a signal-to-noise ratio for microarray data, the "Signal-to-Noise Applied to Gene Expression Experiments", or SNAGEE. SNAGEE is based on the consistency of gene-gene correlations. We applied SNAGEE to a compendium of 80 large datasets on 37 platforms, for a total of 24,380 samples, and assessed the signal-to-noise ratio of studies and samples. This allowed us to discover serious issues with three studies. We show that signal-to-noise ratios of both studies and samples are linked to the statistical significance of the biological results.
conclusionsWe showed that SNAGEE is an effective way to measure data quality for most types of gene expression studies, and that it often outperforms existing techniques. Furthermore, SNAGEE is platform-independent and does not require raw data files. The SNAGEE R package is available in BioConductor.
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